Overview

Dataset statistics

Number of variables25
Number of observations1419910
Missing cells0
Missing cells (%)0.0%
Duplicate rows136643
Duplicate rows (%)9.6%
Total size in memory270.8 MiB
Average record size in memory200.0 B

Variable types

Numeric6
Categorical19

Alerts

is_Origination_channel_tpo has constant value "0.0"Constant
Dataset has 136643 (9.6%) duplicate rowsDuplicates
CLoan_to_value is highly overall correlated with Mortgage_Insurance and 2 other fieldsHigh correlation
Mortgage_Insurance is highly overall correlated with CLoan_to_value and 2 other fieldsHigh correlation
OLoan_to_value is highly overall correlated with CLoan_to_value and 2 other fieldsHigh correlation
is_First_time_homeowner is highly overall correlated with is_First_time_homeowner_No and 1 other fieldsHigh correlation
is_First_time_homeowner_No is highly overall correlated with is_First_time_homeowner and 1 other fieldsHigh correlation
is_Loan_purpose_cash is highly overall correlated with is_Loan_purpose_purcHigh correlation
is_Loan_purpose_noca is highly overall correlated with is_Loan_purpose_purcHigh correlation
is_Loan_purpose_purc is highly overall correlated with CLoan_to_value and 6 other fieldsHigh correlation
is_Occupancy_status_inve is highly overall correlated with is_Occupancy_status_primHigh correlation
is_Occupancy_status_prim is highly overall correlated with is_Occupancy_status_inve and 1 other fieldsHigh correlation
is_Occupancy_status_seco is highly overall correlated with is_Occupancy_status_primHigh correlation
is_Origination_channel_corr is highly overall correlated with is_Origination_channel_retaHigh correlation
is_Origination_channel_reta is highly overall correlated with is_Origination_channel_corrHigh correlation
is_Property_type_pud is highly overall correlated with is_Property_type_singHigh correlation
is_Property_type_sing is highly overall correlated with is_Property_type_pudHigh correlation
is_Occupancy_status_prim is highly imbalanced (59.5%)Imbalance
is_Occupancy_status_inve is highly imbalanced (69.2%)Imbalance
is_Occupancy_status_seco is highly imbalanced (82.8%)Imbalance
is_Origination_channel_brok is highly imbalanced (50.9%)Imbalance
is_Property_type_cond is highly imbalanced (63.3%)Imbalance
is_Property_type_coop is highly imbalanced (98.7%)Imbalance
is_Property_type_manu is highly imbalanced (95.5%)Imbalance
DFlag is highly imbalanced (82.6%)Imbalance
Mortgage_Insurance has 958785 (67.5%) zerosZeros

Reproduction

Analysis started2025-12-05 03:39:29.883467
Analysis finished2025-12-05 03:40:23.372159
Duration53.49 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

Credit_Score
Real number (ℝ)

Distinct524
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-3.6894518 × 10-16
Minimum-14.74573
Maximum1.3256295
Zeros0
Zeros (%)0.0%
Negative607258
Negative (%)42.8%
Memory size10.8 MiB
2025-12-04T22:40:23.422876image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-14.74573
5-th percentile-2.0203309
Q1-0.57888198
median0.22301507
Q30.80933917
95-th percentile1.21584
Maximum1.3256295
Range16.071359
Interquartile range (IQR)1.3882212

Descriptive statistics

Standard deviation1.0000004
Coefficient of variation (CV)-2.7104307 × 1015
Kurtosis0.017736332
Mean-3.6894518 × 10-16
Median Absolute Deviation (MAD)0.6661343
Skewness-0.8273442
Sum-5.0567905 × 10-10
Variance1.0000007
MonotonicityNot monotonic
2025-12-04T22:40:23.475650image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.21583999611660
 
0.8%
1.13946884810846
 
0.8%
1.17765442210821
 
0.8%
1.10128327410805
 
0.8%
1.02491212610693
 
0.8%
0.948540977810396
 
0.7%
0.98672655189915
 
0.7%
1.2922111449817
 
0.7%
1.06309779812
 
0.7%
1.254025579629
 
0.7%
Other values (514)1315516
92.6%
ValueCountFrequency (%)
-14.745729941
 
< 0.1%
-6.8785956431
 
< 0.1%
-4.4300302211
 
< 0.1%
-3.743999781
 
< 0.1%
-3.70500263110
 
< 0.1%
-3.69157398311
 
< 0.1%
-3.66600548215
< 0.1%
-3.6527078659
 
< 0.1%
-3.63372790233
< 0.1%
-3.62700833412
 
< 0.1%
ValueCountFrequency (%)
1.3256295444399
0.3%
1.3250573377091
0.5%
1.3221552144164
0.3%
1.2922111449817
0.7%
1.2882203257042
0.5%
1.2866323954241
0.3%
1.2832890974183
0.3%
1.254025579629
0.7%
1.2513833138206
0.6%
1.2476352465112
0.4%

Mortgage_Insurance
Real number (ℝ)

High correlation  Zeros 

Distinct25
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.0596319
Minimum0
Maximum52
Zeros958785
Zeros (%)67.5%
Negative0
Negative (%)0.0%
Memory size10.8 MiB
2025-12-04T22:40:23.548408image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q325
95-th percentile30
Maximum52
Range52
Interquartile range (IQR)25

Descriptive statistics

Standard deviation12.167109
Coefficient of variation (CV)1.5096358
Kurtosis-0.93771274
Mean8.0596319
Median Absolute Deviation (MAD)0
Skewness0.95634713
Sum11443952
Variance148.03853
MonotonicityNot monotonic
2025-12-04T22:40:23.593817image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0958785
67.5%
25192277
 
13.5%
30182499
 
12.9%
1260356
 
4.3%
189751
 
0.7%
66494
 
0.5%
166166
 
0.4%
353520
 
0.2%
2027
 
< 0.1%
178
 
< 0.1%
Other values (15)27
 
< 0.1%
ValueCountFrequency (%)
0958785
67.5%
66494
 
0.5%
101
 
< 0.1%
1260356
 
4.3%
131
 
< 0.1%
153
 
< 0.1%
166166
 
0.4%
178
 
< 0.1%
189751
 
0.7%
2027
 
< 0.1%
ValueCountFrequency (%)
521
 
< 0.1%
501
 
< 0.1%
431
 
< 0.1%
402
 
< 0.1%
391
 
< 0.1%
353520
 
0.2%
341
 
< 0.1%
331
 
< 0.1%
324
 
< 0.1%
30182499
12.9%

Number_of_units
Real number (ℝ)

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.6397563 × 10-16
Minimum-0.13364156
Maximum14.170713
Zeros0
Zeros (%)0.0%
Negative1391363
Negative (%)98.0%
Memory size10.8 MiB
2025-12-04T22:40:23.640970image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-0.13364156
5-th percentile-0.13364156
Q1-0.13364156
median-0.12920909
Q3-0.12333579
95-th percentile-0.1232318
Maximum14.170713
Range14.304355
Interquartile range (IQR)0.010305765

Descriptive statistics

Standard deviation1.0000004
Coefficient of variation (CV)-6.0984692 × 1015
Kurtosis103.53133
Mean-1.6397563 × 10-16
Median Absolute Deviation (MAD)0.0044324623
Skewness9.505424
Sum-2.1282176 × 10-10
Variance1.0000007
MonotonicityNot monotonic
2025-12-04T22:40:23.681635image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
-0.1292090949515247
36.3%
-0.1336415571401795
28.3%
-0.1233357922237293
16.7%
-0.1232317996237028
16.7%
4.6374317137918
 
0.6%
4.278022856425
 
0.5%
4.4855270173155
 
0.2%
4.4439463193094
 
0.2%
9.404072521698
 
0.1%
8.6896872581498
 
0.1%
Other values (6)4759
 
0.3%
ValueCountFrequency (%)
-0.1336415571401795
28.3%
-0.1292090949515247
36.3%
-0.1233357922237293
16.7%
-0.1232317996237028
16.7%
4.278022856425
 
0.5%
4.4439463193094
 
0.2%
4.4855270173155
 
0.2%
4.6374317137918
 
0.6%
8.6896872581498
 
0.1%
9.011124438804
 
0.1%
ValueCountFrequency (%)
14.17071333980
 
0.1%
13.70325264590
 
< 0.1%
13.57830256605
 
< 0.1%
13.101351671007
 
0.1%
9.404072521698
 
0.1%
9.094389826773
 
0.1%
9.011124438804
 
0.1%
8.6896872581498
 
0.1%
4.6374317137918
0.6%
4.4855270173155
 
0.2%

CLoan_to_value
Real number (ℝ)

High correlation 

Distinct103
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.76455404
Minimum0.04
Maximum1.17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.8 MiB
2025-12-04T22:40:23.748261image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.04
5-th percentile0.44
Q10.69
median0.8
Q30.9
95-th percentile0.95
Maximum1.17
Range1.13
Interquartile range (IQR)0.21

Descriptive statistics

Standard deviation0.16076872
Coefficient of variation (CV)0.21027777
Kurtosis0.91379857
Mean0.76455404
Median Absolute Deviation (MAD)0.1
Skewness-0.98530322
Sum1085597.9
Variance0.025846581
MonotonicityNot monotonic
2025-12-04T22:40:23.821095image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.8296165
20.9%
0.95200784
 
14.1%
0.994195
 
6.6%
0.7586399
 
6.1%
0.9757430
 
4.0%
0.749882
 
3.5%
0.8536465
 
2.6%
0.628662
 
2.0%
0.7924337
 
1.7%
0.7421907
 
1.5%
Other values (93)523684
36.9%
ValueCountFrequency (%)
0.042
 
< 0.1%
0.059
 
< 0.1%
0.0618
 
< 0.1%
0.0727
 
< 0.1%
0.0852
 
< 0.1%
0.0979
 
< 0.1%
0.1117
< 0.1%
0.11154
< 0.1%
0.12229
< 0.1%
0.13245
< 0.1%
ValueCountFrequency (%)
1.171
 
< 0.1%
1.05110
 
< 0.1%
1.04229
 
< 0.1%
1.03353
 
< 0.1%
1.02918
 
0.1%
1.011164
 
0.1%
11583
 
0.1%
0.99401
 
< 0.1%
0.98272
 
< 0.1%
0.9757430
4.0%

Debt_to_income
Real number (ℝ)

Distinct53
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.631875
Minimum1
Maximum54
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.8 MiB
2025-12-04T22:40:23.881459image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile21
Q131
median38
Q343
95-th percentile48
Maximum54
Range53
Interquartile range (IQR)12

Descriptive statistics

Standard deviation8.4456777
Coefficient of variation (CV)0.23055543
Kurtosis-0.039179135
Mean36.631875
Median Absolute Deviation (MAD)6
Skewness-0.6831391
Sum52013965
Variance71.329471
MonotonicityNot monotonic
2025-12-04T22:40:23.960193image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4594512
 
6.7%
4489529
 
6.3%
4379430
 
5.6%
4273239
 
5.2%
4168462
 
4.8%
4065315
 
4.6%
3962323
 
4.4%
3859149
 
4.2%
3756326
 
4.0%
3653227
 
3.7%
Other values (43)718398
50.6%
ValueCountFrequency (%)
192
 
< 0.1%
294
 
< 0.1%
3126
 
< 0.1%
4197
 
< 0.1%
5254
 
< 0.1%
6342
 
< 0.1%
7453
 
< 0.1%
8642
< 0.1%
9901
0.1%
101255
0.1%
ValueCountFrequency (%)
541
 
< 0.1%
531
 
< 0.1%
512
 
< 0.1%
5031353
 
2.2%
4934237
 
2.4%
4830742
 
2.2%
4730227
 
2.1%
4630844
 
2.2%
4594512
6.7%
4489529
6.3%

OLoan_to_value
Real number (ℝ)

High correlation 

Distinct95
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.76162543
Minimum0.04
Maximum1.17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.8 MiB
2025-12-04T22:40:24.020800image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.04
5-th percentile0.44
Q10.69
median0.8
Q30.9
95-th percentile0.95
Maximum1.17
Range1.13
Interquartile range (IQR)0.21

Descriptive statistics

Standard deviation0.16116869
Coefficient of variation (CV)0.21161149
Kurtosis0.86506749
Mean0.76162543
Median Absolute Deviation (MAD)0.1
Skewness-0.96906137
Sum1081439.6
Variance0.025975348
MonotonicityNot monotonic
2025-12-04T22:40:24.095599image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.8298038
21.0%
0.95199435
 
14.0%
0.988782
 
6.3%
0.7587863
 
6.2%
0.9760372
 
4.3%
0.750899
 
3.6%
0.8535411
 
2.5%
0.629478
 
2.1%
0.7924321
 
1.7%
0.7422193
 
1.6%
Other values (85)523118
36.8%
ValueCountFrequency (%)
0.042
 
< 0.1%
0.059
 
< 0.1%
0.0618
 
< 0.1%
0.0729
 
< 0.1%
0.0853
 
< 0.1%
0.0982
 
< 0.1%
0.1121
< 0.1%
0.11156
< 0.1%
0.12236
< 0.1%
0.13249
< 0.1%
ValueCountFrequency (%)
1.171
 
< 0.1%
0.9760372
 
4.3%
0.96984
 
0.1%
0.95199435
14.0%
0.948455
 
0.6%
0.938778
 
0.6%
0.928620
 
0.6%
0.913291
 
0.2%
0.988782
6.3%
0.898819
 
0.6%

Single_borrower
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size81.2 MiB
1.0
723561 
0.0
696349 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4259730
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.0723561
51.0%
0.0696349
49.0%

Length

2025-12-04T22:40:24.161674image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-04T22:40:24.198400image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0723561
51.0%
0.0696349
49.0%

Most occurring characters

ValueCountFrequency (%)
02116259
49.7%
.1419910
33.3%
1723561
 
17.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)4259730
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
02116259
49.7%
.1419910
33.3%
1723561
 
17.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4259730
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
02116259
49.7%
.1419910
33.3%
1723561
 
17.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4259730
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
02116259
49.7%
.1419910
33.3%
1723561
 
17.0%

is_Loan_purpose_purc
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size81.2 MiB
1.0
730081 
0.0
689829 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4259730
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0730081
51.4%
0.0689829
48.6%

Length

2025-12-04T22:40:24.230030image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-04T22:40:24.276084image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0730081
51.4%
0.0689829
48.6%

Most occurring characters

ValueCountFrequency (%)
02109739
49.5%
.1419910
33.3%
1730081
 
17.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)4259730
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
02109739
49.5%
.1419910
33.3%
1730081
 
17.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4259730
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
02109739
49.5%
.1419910
33.3%
1730081
 
17.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4259730
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
02109739
49.5%
.1419910
33.3%
1730081
 
17.1%

is_Loan_purpose_cash
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size81.2 MiB
0.0
1024925 
1.0
394985 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4259730
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.01024925
72.2%
1.0394985
 
27.8%

Length

2025-12-04T22:40:24.308659image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-04T22:40:24.345921image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.01024925
72.2%
1.0394985
 
27.8%

Most occurring characters

ValueCountFrequency (%)
02444835
57.4%
.1419910
33.3%
1394985
 
9.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)4259730
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
02444835
57.4%
.1419910
33.3%
1394985
 
9.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4259730
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
02444835
57.4%
.1419910
33.3%
1394985
 
9.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4259730
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
02444835
57.4%
.1419910
33.3%
1394985
 
9.3%

is_Loan_purpose_noca
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size81.2 MiB
0.0
1125066 
1.0
294844 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4259730
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.01125066
79.2%
1.0294844
 
20.8%

Length

2025-12-04T22:40:24.394294image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-04T22:40:24.423701image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.01125066
79.2%
1.0294844
 
20.8%

Most occurring characters

ValueCountFrequency (%)
02544976
59.7%
.1419910
33.3%
1294844
 
6.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)4259730
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
02544976
59.7%
.1419910
33.3%
1294844
 
6.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4259730
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
02544976
59.7%
.1419910
33.3%
1294844
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4259730
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
02544976
59.7%
.1419910
33.3%
1294844
 
6.9%

is_First_time_homeowner
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size81.2 MiB
0.0
1069851 
1.0
350059 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4259730
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.01069851
75.3%
1.0350059
 
24.7%

Length

2025-12-04T22:40:24.469091image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-04T22:40:24.511588image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.01069851
75.3%
1.0350059
 
24.7%

Most occurring characters

ValueCountFrequency (%)
02489761
58.4%
.1419910
33.3%
1350059
 
8.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)4259730
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
02489761
58.4%
.1419910
33.3%
1350059
 
8.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4259730
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
02489761
58.4%
.1419910
33.3%
1350059
 
8.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4259730
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
02489761
58.4%
.1419910
33.3%
1350059
 
8.2%

is_First_time_homeowner_No
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size81.2 MiB
1.0
1069851 
0.0
350059 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4259730
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.01069851
75.3%
0.0350059
 
24.7%

Length

2025-12-04T22:40:24.553972image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-04T22:40:24.592010image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.01069851
75.3%
0.0350059
 
24.7%

Most occurring characters

ValueCountFrequency (%)
01769969
41.6%
.1419910
33.3%
11069851
25.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)4259730
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
01769969
41.6%
.1419910
33.3%
11069851
25.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4259730
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
01769969
41.6%
.1419910
33.3%
11069851
25.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4259730
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
01769969
41.6%
.1419910
33.3%
11069851
25.1%

is_Occupancy_status_prim
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size81.2 MiB
1.0
1305139 
0.0
 
114771

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4259730
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.01305139
91.9%
0.0114771
 
8.1%

Length

2025-12-04T22:40:24.630397image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-04T22:40:24.660064image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.01305139
91.9%
0.0114771
 
8.1%

Most occurring characters

ValueCountFrequency (%)
01534681
36.0%
.1419910
33.3%
11305139
30.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)4259730
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
01534681
36.0%
.1419910
33.3%
11305139
30.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4259730
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
01534681
36.0%
.1419910
33.3%
11305139
30.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4259730
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
01534681
36.0%
.1419910
33.3%
11305139
30.6%

is_Occupancy_status_inve
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size81.2 MiB
0.0
1341596 
1.0
 
78314

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4259730
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.01341596
94.5%
1.078314
 
5.5%

Length

2025-12-04T22:40:24.709182image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-04T22:40:24.741742image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.01341596
94.5%
1.078314
 
5.5%

Most occurring characters

ValueCountFrequency (%)
02761506
64.8%
.1419910
33.3%
178314
 
1.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)4259730
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
02761506
64.8%
.1419910
33.3%
178314
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4259730
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
02761506
64.8%
.1419910
33.3%
178314
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4259730
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
02761506
64.8%
.1419910
33.3%
178314
 
1.8%

is_Occupancy_status_seco
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size81.2 MiB
0.0
1383453 
1.0
 
36457

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4259730
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.01383453
97.4%
1.036457
 
2.6%

Length

2025-12-04T22:40:24.790364image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-04T22:40:24.826764image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.01383453
97.4%
1.036457
 
2.6%

Most occurring characters

ValueCountFrequency (%)
02803363
65.8%
.1419910
33.3%
136457
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)4259730
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
02803363
65.8%
.1419910
33.3%
136457
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4259730
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
02803363
65.8%
.1419910
33.3%
136457
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4259730
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
02803363
65.8%
.1419910
33.3%
136457
 
0.9%

is_Origination_channel_reta
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size81.2 MiB
1.0
805948 
0.0
613962 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4259730
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.0805948
56.8%
0.0613962
43.2%

Length

2025-12-04T22:40:24.859940image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-04T22:40:24.906034image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0805948
56.8%
0.0613962
43.2%

Most occurring characters

ValueCountFrequency (%)
02033872
47.7%
.1419910
33.3%
1805948
 
18.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)4259730
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
02033872
47.7%
.1419910
33.3%
1805948
 
18.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4259730
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
02033872
47.7%
.1419910
33.3%
1805948
 
18.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4259730
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
02033872
47.7%
.1419910
33.3%
1805948
 
18.9%

is_Origination_channel_brok
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size81.2 MiB
0.0
1268065 
1.0
151845 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4259730
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.01268065
89.3%
1.0151845
 
10.7%

Length

2025-12-04T22:40:24.953866image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-04T22:40:24.984146image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.01268065
89.3%
1.0151845
 
10.7%

Most occurring characters

ValueCountFrequency (%)
02687975
63.1%
.1419910
33.3%
1151845
 
3.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)4259730
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
02687975
63.1%
.1419910
33.3%
1151845
 
3.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4259730
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
02687975
63.1%
.1419910
33.3%
1151845
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4259730
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
02687975
63.1%
.1419910
33.3%
1151845
 
3.6%

is_Origination_channel_corr
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size81.2 MiB
0.0
957796 
1.0
462114 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4259730
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0957796
67.5%
1.0462114
32.5%

Length

2025-12-04T22:40:25.026500image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-04T22:40:25.068919image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0957796
67.5%
1.0462114
32.5%

Most occurring characters

ValueCountFrequency (%)
02377706
55.8%
.1419910
33.3%
1462114
 
10.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)4259730
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
02377706
55.8%
.1419910
33.3%
1462114
 
10.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4259730
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
02377706
55.8%
.1419910
33.3%
1462114
 
10.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4259730
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
02377706
55.8%
.1419910
33.3%
1462114
 
10.8%

is_Origination_channel_tpo
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size81.2 MiB
0.0
1419910 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4259730
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.01419910
100.0%

Length

2025-12-04T22:40:25.106759image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-04T22:40:25.141651image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.01419910
100.0%

Most occurring characters

ValueCountFrequency (%)
02839820
66.7%
.1419910
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)4259730
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
02839820
66.7%
.1419910
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4259730
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
02839820
66.7%
.1419910
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4259730
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
02839820
66.7%
.1419910
33.3%

is_Property_type_cond
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size81.2 MiB
0.0
1320181 
1.0
 
99729

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4259730
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.01320181
93.0%
1.099729
 
7.0%

Length

2025-12-04T22:40:25.184008image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-04T22:40:25.219885image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.01320181
93.0%
1.099729
 
7.0%

Most occurring characters

ValueCountFrequency (%)
02740091
64.3%
.1419910
33.3%
199729
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)4259730
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
02740091
64.3%
.1419910
33.3%
199729
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4259730
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
02740091
64.3%
.1419910
33.3%
199729
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4259730
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
02740091
64.3%
.1419910
33.3%
199729
 
2.3%

is_Property_type_coop
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size81.2 MiB
0.0
1418263 
1.0
 
1647

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4259730
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.01418263
99.9%
1.01647
 
0.1%

Length

2025-12-04T22:40:25.267252image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-04T22:40:25.299142image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.01418263
99.9%
1.01647
 
0.1%

Most occurring characters

ValueCountFrequency (%)
02838173
66.6%
.1419910
33.3%
11647
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)4259730
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
02838173
66.6%
.1419910
33.3%
11647
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4259730
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
02838173
66.6%
.1419910
33.3%
11647
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4259730
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
02838173
66.6%
.1419910
33.3%
11647
 
< 0.1%

is_Property_type_manu
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size81.2 MiB
0.0
1412858 
1.0
 
7052

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4259730
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.01412858
99.5%
1.07052
 
0.5%

Length

2025-12-04T22:40:25.345276image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-04T22:40:25.376312image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.01412858
99.5%
1.07052
 
0.5%

Most occurring characters

ValueCountFrequency (%)
02832768
66.5%
.1419910
33.3%
17052
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)4259730
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
02832768
66.5%
.1419910
33.3%
17052
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4259730
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
02832768
66.5%
.1419910
33.3%
17052
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4259730
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
02832768
66.5%
.1419910
33.3%
17052
 
0.2%

is_Property_type_pud
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size81.2 MiB
0.0
1059606 
1.0
360304 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4259730
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.01059606
74.6%
1.0360304
 
25.4%

Length

2025-12-04T22:40:25.416310image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-04T22:40:25.462816image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.01059606
74.6%
1.0360304
 
25.4%

Most occurring characters

ValueCountFrequency (%)
02479516
58.2%
.1419910
33.3%
1360304
 
8.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)4259730
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
02479516
58.2%
.1419910
33.3%
1360304
 
8.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4259730
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
02479516
58.2%
.1419910
33.3%
1360304
 
8.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4259730
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
02479516
58.2%
.1419910
33.3%
1360304
 
8.5%

is_Property_type_sing
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size81.2 MiB
1.0
951178 
0.0
468732 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4259730
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0951178
67.0%
0.0468732
33.0%

Length

2025-12-04T22:40:25.499069image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-04T22:40:25.534953image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0951178
67.0%
0.0468732
33.0%

Most occurring characters

ValueCountFrequency (%)
01888642
44.3%
.1419910
33.3%
1951178
22.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)4259730
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
01888642
44.3%
.1419910
33.3%
1951178
22.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4259730
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
01888642
44.3%
.1419910
33.3%
1951178
22.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4259730
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
01888642
44.3%
.1419910
33.3%
1951178
22.3%

DFlag
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size81.2 MiB
0.0
1383037 
1.0
 
36873

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4259730
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.01383037
97.4%
1.036873
 
2.6%

Length

2025-12-04T22:40:25.585208image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-04T22:40:25.622262image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.01383037
97.4%
1.036873
 
2.6%

Most occurring characters

ValueCountFrequency (%)
02802947
65.8%
.1419910
33.3%
136873
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)4259730
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
02802947
65.8%
.1419910
33.3%
136873
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4259730
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
02802947
65.8%
.1419910
33.3%
136873
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4259730
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
02802947
65.8%
.1419910
33.3%
136873
 
0.9%

Interactions

2025-12-04T22:40:18.753089image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-04T22:40:12.445055image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-04T22:40:13.755597image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-04T22:40:14.971052image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-04T22:40:16.217748image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-04T22:40:17.490170image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-04T22:40:18.974232image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-04T22:40:12.673548image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-04T22:40:13.957899image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-04T22:40:15.187773image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-04T22:40:16.436056image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-04T22:40:17.708054image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-04T22:40:19.192316image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-04T22:40:12.885595image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-04T22:40:14.163849image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-04T22:40:15.385579image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-04T22:40:16.648462image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-04T22:40:17.914015image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-04T22:40:19.396319image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-04T22:40:13.103693image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-04T22:40:14.362198image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-04T22:40:15.599670image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-04T22:40:16.854035image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-04T22:40:18.126047image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-04T22:40:19.606164image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-04T22:40:13.315932image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-04T22:40:14.569913image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-04T22:40:15.805628image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-04T22:40:17.060729image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-04T22:40:18.331986image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-04T22:40:19.822411image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-04T22:40:13.546432image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-04T22:40:14.767423image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-04T22:40:16.011681image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-04T22:40:17.273726image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-04T22:40:18.544048image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-12-04T22:40:25.666249image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
CLoan_to_valueCredit_ScoreDFlagDebt_to_incomeMortgage_InsuranceNumber_of_unitsOLoan_to_valueSingle_borroweris_First_time_homeowneris_First_time_homeowner_Nois_Loan_purpose_cashis_Loan_purpose_nocais_Loan_purpose_purcis_Occupancy_status_inveis_Occupancy_status_primis_Occupancy_status_secois_Origination_channel_brokis_Origination_channel_corris_Origination_channel_retais_Property_type_condis_Property_type_coopis_Property_type_manuis_Property_type_pudis_Property_type_sing
CLoan_to_value1.0000.1370.0330.0170.810-0.0780.9870.0270.4640.4640.4600.2300.5590.1570.1660.0810.0070.0860.0820.0390.0190.0160.0640.076
Credit_Score0.1371.0000.0430.0080.1090.0010.1330.0140.0330.0330.0900.0250.0610.0270.0270.0080.0350.0220.0430.0130.0010.0100.0360.039
DFlag0.0330.0431.0000.0480.0320.0080.0320.0330.0170.0170.0050.0040.0080.0040.0060.0040.0000.0330.0320.0040.0000.0010.0060.007
Debt_to_income0.0170.0080.0481.0000.016-0.0570.0170.0490.0180.0180.0260.0670.0390.0570.0610.0300.0400.0200.0380.0270.0080.0160.0280.037
Mortgage_Insurance0.8100.1090.0320.0161.000-0.0690.8170.0670.4490.4490.4300.2210.5140.1580.1640.0670.0300.0820.0720.0580.0120.0110.0460.052
Number_of_units-0.0780.0010.008-0.057-0.0691.000-0.0820.0110.0400.0400.0420.0060.0330.2550.2000.0230.0270.0030.0160.0390.0050.0100.0820.099
OLoan_to_value0.9870.1330.0320.0170.817-0.0821.0000.0230.4640.4640.4510.2330.5600.1540.1620.0810.0070.0820.0780.0400.0190.0150.0590.072
Single_borrower0.0270.0140.0330.0490.0670.0110.0231.0000.0250.0250.0120.0120.0010.0150.0000.0210.0050.0020.0000.0720.0090.0030.0250.017
is_First_time_homeowner0.4640.0330.0170.0180.4490.0400.4640.0251.0001.0000.3550.2930.5560.1380.1700.0930.0240.0550.0670.1020.0300.0060.0060.053
is_First_time_homeowner_No0.4640.0330.0170.0180.4490.0400.4640.0251.0001.0000.3550.2930.5560.1380.1700.0930.0240.0550.0670.1020.0300.0060.0060.053
is_Loan_purpose_cash0.4600.0900.0050.0260.4300.0420.4510.0120.3550.3551.0000.3180.6390.0630.0270.0440.0330.0870.1020.0710.0070.0130.0840.119
is_Loan_purpose_noca0.2300.0250.0040.0670.2210.0060.2330.0120.2930.2930.3181.0000.5270.0230.0390.0340.0210.0330.0180.0460.0130.0040.0090.034
is_Loan_purpose_purc0.5590.0610.0080.0390.5140.0330.5600.0010.5560.5560.6390.5271.0000.0370.0070.0670.0120.1040.1070.1010.0170.0080.0830.134
is_Occupancy_status_inve0.1570.0270.0040.0570.1580.2550.1540.0150.1380.1380.0630.0230.0371.0000.8150.0390.0250.0130.0040.0260.0080.0170.0440.030
is_Occupancy_status_prim0.1660.0270.0060.0610.1640.2000.1620.0000.1700.1700.0270.0390.0070.8151.0000.5470.0170.0100.0010.0530.0080.0080.0300.001
is_Occupancy_status_seco0.0810.0080.0040.0300.0670.0230.0810.0210.0930.0930.0440.0340.0670.0390.5471.0000.0060.0000.0030.0530.0020.0100.0120.041
is_Origination_channel_brok0.0070.0350.0000.0400.0300.0270.0070.0050.0240.0240.0330.0210.0120.0250.0170.0061.0000.2400.3960.0340.0080.0160.0000.016
is_Origination_channel_corr0.0860.0220.0330.0200.0820.0030.0820.0020.0550.0550.0870.0330.1040.0130.0100.0000.2401.0000.7960.0040.0090.0320.0480.041
is_Origination_channel_reta0.0820.0430.0320.0380.0720.0160.0780.0000.0670.0670.1020.0180.1070.0040.0010.0030.3960.7961.0000.0250.0040.0400.0450.049
is_Property_type_cond0.0390.0130.0040.0270.0580.0390.0400.0720.1020.1020.0710.0460.1010.0260.0530.0530.0340.0040.0251.0000.0090.0190.1600.392
is_Property_type_coop0.0190.0010.0000.0080.0120.0050.0190.0090.0300.0300.0070.0130.0170.0080.0080.0020.0080.0090.0040.0091.0000.0020.0200.049
is_Property_type_manu0.0160.0100.0010.0160.0110.0100.0150.0030.0060.0060.0130.0040.0080.0170.0080.0100.0160.0320.0400.0190.0021.0000.0410.101
is_Property_type_pud0.0640.0360.0060.0280.0460.0820.0590.0250.0060.0060.0840.0090.0830.0440.0300.0120.0000.0480.0450.1600.0200.0411.0000.831
is_Property_type_sing0.0760.0390.0070.0370.0520.0990.0720.0170.0530.0530.1190.0340.1340.0300.0010.0410.0160.0410.0490.3920.0490.1010.8311.000

Missing values

2025-12-04T22:40:20.198255image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-12-04T22:40:21.329665image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Credit_ScoreMortgage_InsuranceNumber_of_unitsCLoan_to_valueDebt_to_incomeOLoan_to_valueSingle_borroweris_Loan_purpose_purcis_Loan_purpose_cashis_Loan_purpose_nocais_First_time_homeowneris_First_time_homeowner_Nois_Occupancy_status_primis_Occupancy_status_inveis_Occupancy_status_secois_Origination_channel_retais_Origination_channel_brokis_Origination_channel_corris_Origination_channel_tpois_Property_type_condis_Property_type_coopis_Property_type_manuis_Property_type_pudis_Property_type_singDFlag
00.1557150.0-0.1232320.6829.00.681.00.00.01.00.01.01.00.00.00.01.00.00.01.00.00.00.00.00.0
10.2727070.0-0.1232320.8042.00.801.01.00.00.00.01.01.00.00.01.00.00.00.01.00.00.00.00.00.0
20.0387246.0-0.1232320.8528.00.850.00.00.01.00.01.01.00.00.00.00.01.00.00.00.00.00.01.00.0
31.01365225.0-0.1232320.9044.00.900.01.00.00.00.01.01.00.00.01.00.00.00.00.00.00.00.01.00.0
4-0.46823925.0-0.1232320.9035.00.900.01.00.00.01.00.01.00.00.00.00.01.00.00.00.00.00.01.00.0
5-0.3902450.0-0.1232320.8039.00.800.00.01.00.00.01.01.00.00.01.00.00.00.00.00.00.00.01.00.0
61.16964130.0-0.1232320.9540.00.950.01.00.00.00.01.01.00.00.01.00.00.00.00.00.00.00.01.00.0
70.77966930.0-0.1232320.9540.00.951.01.00.00.01.00.01.00.00.00.00.01.00.00.00.00.00.01.00.0
80.1167180.0-0.1232320.2349.00.231.00.01.00.00.01.01.00.00.01.00.00.00.00.00.00.00.01.00.0
91.2086380.0-0.1232320.5143.00.510.00.00.01.00.01.00.01.00.01.00.00.00.00.00.00.00.01.00.0
Credit_ScoreMortgage_InsuranceNumber_of_unitsCLoan_to_valueDebt_to_incomeOLoan_to_valueSingle_borroweris_Loan_purpose_purcis_Loan_purpose_cashis_Loan_purpose_nocais_First_time_homeowneris_First_time_homeowner_Nois_Occupancy_status_primis_Occupancy_status_inveis_Occupancy_status_secois_Origination_channel_retais_Origination_channel_brokis_Origination_channel_corris_Origination_channel_tpois_Property_type_condis_Property_type_coopis_Property_type_manuis_Property_type_pudis_Property_type_singDFlag
14199000.7576130.0-0.1292090.8048.00.801.01.00.00.00.01.01.00.00.01.00.00.00.00.00.00.00.01.00.0
1419901-1.1516660.0-0.1292090.8031.00.801.01.00.00.01.00.01.00.00.01.00.00.00.00.00.00.00.01.00.0
1419902-0.8843670.0-0.1292090.7731.00.770.00.01.00.00.01.01.00.00.00.00.01.00.00.00.00.00.01.00.0
14199031.0630980.0-0.1292090.8042.00.801.00.01.00.00.01.01.00.00.00.00.01.00.00.00.00.00.01.00.0
1419904-0.0060980.0-0.1292090.6843.00.681.00.01.00.00.01.01.00.00.00.00.01.00.00.00.00.00.01.00.0
14199050.6812420.0-0.1292090.3728.00.370.00.01.00.00.01.01.00.00.00.00.01.00.00.00.00.00.01.00.0
1419906-0.1970260.0-0.1292090.4040.00.400.00.01.00.00.01.01.00.00.00.00.01.00.00.00.00.01.00.00.0
1419907-1.6098920.0-0.1292090.6945.00.690.00.01.00.00.01.01.00.00.01.00.00.00.00.00.00.00.01.00.0
1419908-1.3044080.0-0.1292090.4332.00.430.00.01.00.00.01.01.00.00.01.00.00.00.00.00.00.00.01.00.0
14199090.9485410.0-0.1292090.6342.00.631.00.00.01.00.01.01.00.00.01.00.00.00.00.00.00.00.01.00.0

Duplicate rows

Most frequently occurring

Credit_ScoreMortgage_InsuranceNumber_of_unitsCLoan_to_valueDebt_to_incomeOLoan_to_valueSingle_borroweris_Loan_purpose_purcis_Loan_purpose_cashis_Loan_purpose_nocais_First_time_homeowneris_First_time_homeowner_Nois_Occupancy_status_primis_Occupancy_status_inveis_Occupancy_status_secois_Origination_channel_retais_Origination_channel_brokis_Origination_channel_corris_Origination_channel_tpois_Property_type_condis_Property_type_coopis_Property_type_manuis_Property_type_pudis_Property_type_singDFlag# duplicates
539500.1848290.0-0.1292090.8044.00.801.00.01.00.00.01.01.00.00.01.00.00.00.00.00.00.00.01.00.022
1171541.1012830.0-0.1292090.8045.00.801.00.01.00.00.01.01.00.00.01.00.00.00.00.00.00.00.01.00.020
562510.2230150.0-0.1292090.8044.00.801.00.01.00.00.01.01.00.00.01.00.00.00.00.00.00.00.01.00.018
694330.4139430.0-0.1292090.8045.00.801.00.01.00.00.01.01.00.00.01.00.00.00.00.00.00.00.01.00.018
870290.6812420.0-0.1292090.8044.00.801.00.01.00.00.01.01.00.00.01.00.00.00.00.00.00.00.01.00.017
922780.7576130.0-0.1292090.8044.00.801.00.01.00.00.01.01.00.00.01.00.00.00.00.00.00.00.01.00.017
1268771.21454625.0-0.1336420.9743.00.971.01.00.00.01.00.01.00.00.01.00.00.00.00.00.00.00.01.00.017
743320.4903140.0-0.1292090.8045.00.800.00.01.00.00.01.01.00.00.01.00.00.00.00.00.00.00.01.00.016
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